In the past, a typical application for copiers or scan-to-print image processing systems was to reproduce an input image as accurately as possible, i.e., render a copy. Thus, copies have been rendered as accurately as possible, including flaws in the source image. However, as customers have become more sophisticated in their document reproduction requirements, they have recognized that an exact copy is often not desired. Instead, an inexact copy that is perceived as a higher quality image has been deemed more desirable.
Until recently, image quality from the output of a copier or a scan-to-print system was directly related to the quality of the input image. While this has been satisfactory for reproducing images of text or line drawings, it has been found to be sub-optimal for purposes of reproducing photographs and other more complex images. With photographs, in particular, reproduction is complicated given the inexact nature of the discipline, variations in equipment, aging of photographs, and the like. Given that the input image is often poor quality, it has been deemed desirable to render an output image that is perceived to be the “best” possible image (or at least superior to the input image), rather than an exact copy.
The above-noted patents to Eschbach et al. and Fuss et al. disclose an Automated Image Enhancement (AIE) system. This system receives an optionally sub-sampled description of the input image, and alters the tone reproduction curve (TRC)—a curve that defines the relationship of an input image signal to an output image signal for purposes of enhancement—and/or derives or alters a sharpness filter for that image on an image-by-image basis, as appropriate, so that the resulting output image is perceived to be superior to the input image. AIE is used, for example, to alter perceived exposure, luminance, sharpness, saturation, color balance, and the like to provide an output image that is preferable to the input image. It is important to note that AIE performs well even if it has to rely upon a sub-sampled image to statistically analyze the overall image. AIE does not require that each and every item of image information be analyzed. Of course, this sub-sampling speeds image enhancement operations and reduces the size/number of memory buffers required to implement the AIE system.
More particularly, for example, U.S. Pat. No. 5,414,538 entitled “Image-Dependent Exposure Enhancement” discloses a method of altering the perceived exposure of an output image produced from an input image that includes: (a) receiving the input image defined in terms of red-green-blue (RGB) signals; (b) converting the RGB signals to corresponding luminance-chrominance signals including at least one signal that represent overall image intensity; (c) comparing the intensity signal to upper and lower intensity threshold signals that define the acceptable levels of brightness and darkness in the image; (d) if one of the thresholds is exceeded, the image signal representative of image intensity is processed according to a select equation, and a TRC associated with the image is adjusted so that exposure characteristics of the resulting output image are perceived to be superior to those of the input image.
U.S. Pat. No. 5,450,502 entitled “Image-Dependent Luminance Enhancement” discloses a method of altering the perceived luminance of an output image produced from an input image that includes: (a) receiving the input image data defined in terms of a color space; (b) if required, converting the input image data into a luminance-chrominance color space wherein at least one term bears a relationship to overall intensity of the input image; (c) deriving a global intensity histogram for the overall input image; (d) filtering the histogram signal to flatten high peaks and low valleys without altering its relatively flat portions; and, (e) utilizing the filtered histogram signal to control TRC mapping in a device with which the image is to be rendered. In accordance with another aspect of the disclosed luminance enhancement method, the input image can be divided into plural regions, and a local intensity histogram signal can be derived for each region. If any of the local histogram signals are flatter than the global histogram signal, the local signals are summed and used in place of the global histogram as input to the histogram flattening filter.
The methods described in the Eschbach et al. '538 and '502 patents, and the other Eschbach et al. and Fuss et al. patents noted above, are described in connection with uncompressed image data. However, in many image processing operations, the image data is retrieved from an image storage device or other location or is otherwise supplied in a compressed form to minimize image storage space.
Thus, heretofore, image enhancement operations typically have required that the compressed images first be decompressed or “decoded” for image enhancement operations. This is undesirable in that the decompression operation slows the overall enhancement operation, the image processing apparatus must be provided with additional memory to accommodate large amounts of uncompressed image data, and each compression operation itself, further degrades the image data due to data loss.
Accordingly, it is desirable to provide method and system which overcomes the above-mentioned difficulties and others.